The GPU market for AI is no longer monochrome. On our dedicated hosting you can now provision Nvidia, AMD, or Intel cards with competitive specs for most workloads. The old advice of “just buy Nvidia” is still often right but not universally. Here is the practical comparison.
Contents
- Three software stacks
- Each vendor’s sweet spot
- Where friction lives
- Cost per capability
- Picking a vendor
Three Software Stacks
| Vendor | Primary Stack | Ecosystem |
|---|---|---|
| Nvidia | CUDA | Everything. Every major library, first-party |
| AMD | ROCm | PyTorch, vLLM, Diffusers – mature by 2026 |
| Intel | IPEX-LLM, oneAPI, OpenVINO | LLM inference and production deployment, narrower research support |
Sweet Spots
Nvidia (5090, 6000 Pro): Research workflows that clone new repos weekly. Production serving with vLLM or TGI. Fine-tuning and training. Anywhere CUDA kernels are hand-tuned.
AMD (R9700): Production inference of well-known models. Stable Diffusion. Cost-sensitive workloads where VRAM per pound matters. Increasingly competitive for LLM serving via ROCm vLLM.
Intel (Arc Pro B70): OpenVINO deployments, IPEX-LLM pipelines, power-efficient production. Good at 32 GB capacity without Nvidia pricing.
Friction Points
On Nvidia the friction is cost and availability of high-end cards. Long lead times on 5090 and 6000 Pro through normal channels – less of an issue on dedicated hosting where we source ahead.
On AMD the friction is the “first 10% of repos” problem. The top 90% of AI libraries work on ROCm. The trailing 10% – the experimental repo someone just posted on GitHub – frequently assumes CUDA and needs adaptation. For production of stable models this is a non-issue.
On Intel the friction is documentation and community size. You can run Llama, Qwen, Mistral, SDXL, and Whisper on Intel. You may hit rough edges on less common models.
Vendor-Neutral Dedicated Hosting
Nvidia, AMD, and Intel cards on the same hosting platform with fixed UK monthly pricing.
Browse GPU ServersCost per Capability
At the 32 GB tier, Intel and AMD are typically 20-35% cheaper than comparable Nvidia options. At the 8-16 GB tier, Nvidia is roughly competitive because mass-market consumer cards keep prices down. At the 96 GB tier, Nvidia’s 6000 Pro is the only practical single-card option. The AMD option in this class (Ryzen AI Max+ 395 unified) is very different silicon and not a direct replacement.
Picking a Vendor
Three questions decide it. First: do you clone experimental AI repos regularly? If yes, Nvidia. Second: are your models stable and well-known, and is VRAM more important than speed? AMD or Intel both pay back at the 32 GB tier. Third: do you need 96 GB on one card? Nvidia 6000 Pro.
For tier-specific matchups see R9700 vs RTX 5080, B70 vs RTX 5080, and R9700 vs B70.
